Intro

Background Information: We were tasked by Kirk Bogard, the Associate Vice President for Development and External Relations at Miami University to explore a dataset of real student data in order to find relationships and patterns that he can use to give Miami a competitive advantage. After exploring the data, we found a few particular variables that can help us find a potential relationship in the dataset, survey_salary, survery_internships, and survey_state. We plan to build a regression model using the number of internships during college to predict salary after graduation, using state to control for salary. The purpose of this analysis is to provide information on the relationship between number of internships and salary information to FSB Career Services. This will help them give more accurate guidance to students to ensure they get the best full time opportunity for them.

Survey Overview:

Internship Effects on Salary:

External Research:

Survey Overview

Overview of survey responses

row

Usable Response %

rate = round(100 * nrow(df)/nrow(df2),0)

gauge(rate, min=0, max=100, symbol='%', gaugeSectors(
  success=c(80,100), warning= c(40,79), danger=c(0,39)
)
      )

Usable Responses

1728

row

Distribution of Number of Internships

Internship Effects on Salary

Column

Mean Salary by Number of Internships

Column

Regression Model Predicting Salary by Number of Internships


Call:
lm(formula = df$survey_salary ~ df$survey_internships)

Residuals:
   Min     1Q Median     3Q    Max 
-51384  -6755    245   5745 115713 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             51511.7      970.6  53.071  < 2e-16 ***
df$survey_internships1   5243.0     1086.3   4.826 1.51e-06 ***
df$survey_internships2   7775.3     1065.8   7.296 4.52e-13 ***
df$survey_internships3   9872.5     1220.7   8.088 1.14e-15 ***
df$survey_internships4   9893.8     2207.3   4.482 7.87e-06 ***
df$survey_internships5   9388.3     4258.6   2.205   0.0276 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 11730 on 1722 degrees of freedom
Multiple R-squared:  0.04643,   Adjusted R-squared:  0.04366 
F-statistic: 16.77 on 5 and 1722 DF,  p-value: 3.286e-16